Prevalence and association with environmental factors and establishment of prediction model of atopic dermatitis in pet dogs in China

Yingbo Dong; Long Wang; Kai Zhang; Haibin Zhang; Dawei Guo

doi:10.3389/fvets.2024.1428805

Prevalence and association with environmental factors and establishment of prediction model of atopic dermatitis in pet dogs in China

Front Vet Sci. 2024 Sep 25:11:1428805. doi: 10.3389/fvets.2024.1428805. eCollection 2024.

Authors

Yingbo Dong^{1

2

3}, Long Wang^{1

2}, Kai Zhang⁴, Haibin Zhang¹, Dawei Guo^{1

2}

Affiliations

¹ MOE Joint International Research Laboratory of Animal Health and Food Safety, College of Veterinary Medicine, Nanjing Agricultural University, Nanjing, China.
² Engineering Center of Innovative Veterinary Drugs, Center for Veterinary Drug Research and Evaluation, College of Veterinary Medicine, Nanjing Agricultural University, Nanjing, China.
³ New Ruipeng Pet Healthcare Group Co., Ltd., Nanjing, China.
⁴ Pancreas Center, First Affiliated Hospital of Nanjing Medical University, Nanjing, China.

Abstract

Canine atopic dermatitis (CAD) is a common skin disease in dogs. Various pathogenic factors contribute to CAD, with dust mites, environmental pathogens, and other substances being predominant. This research involved comprehensive statistical analysis and prediction of CAD in China, using data from 14 cities. A distributed lag nonlinear model (DLNM) was developed to evaluate the impact of environmental factors on CAD incidence. Additionally, a seasonal auto-regressive moving average (ARIMA) model was used to forecast the monthly number of CAD cases. The findings indicated that CAD mainly occurs during June, July, August, and September in China. There was a positive correlation found between CAD incidence and temperature and humidity, while a negative correlation was observed with CO, PM2.5, and other pollutants.

Keywords: canine atopic dermatitis; climatic factor; model prediction; prevention of disease; regional factors.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Fundamental Research Funds for the Central Universities (KYCXJC2024002 & YDZX2023006) and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).